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- Tasks: Join a dynamic team to build and maintain data science infrastructure.
- Company: SPG Resourcing values diversity and fosters an inclusive workplace.
- Benefits: Enjoy flexible working options and a collaborative environment.
- Why this job: Make a real impact by transitioning models from research to production.
- Qualifications: Proficiency in Python, Databricks, and Azure is essential.
- Other info: We support reasonable accommodations throughout the application process.
The predicted salary is between 43200 - 72000 £ per year.
This position is for an experienced Machine Learning Engineer to join a newly established data science team. The primary focus is on building and maintaining the infrastructure to support the full data science lifecycle from data ingestion to model deployment, monitoring, and upgrades within Azure and Databricks environments. The engineer will work closely with data scientists in a collaborative, cross-functional setting, helping transition models from research into production.
Key Responsibilities:
- Own and develop deployment frameworks for data science services.
- Ownership of the deployment framework for all data science services, overseeing how data will flow into the data science life cycle from the wider business data warehouse.
- Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when moving to production.
- Automate the data science pipeline (data prep to deployment).
- Collaborate with cross-functional teams to ensure smooth productionisation of models.
- Write clean, production-ready Python code.
- Apply software engineering best practices, CI/CD, TDD.
Required Skills:
- Proficiency in Python, Databricks, and Azure.
- Experience with deployment tools (e.g., AKS, managed endpoints).
- Strong software engineering background (CI/CD, VCS, TDD).
- Ability to integrate ML into business workflows.
Desirable:
- Background in quantitative disciplines (math, stats, physics).
- Experience in finance, insurance, or ecommerce.
- Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn.
If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com
SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Azure and Databricks, as these are key platforms for the role. Consider taking online courses or tutorials to deepen your understanding of how to deploy machine learning models in these environments.
✨Tip Number 2
Showcase your experience with CI/CD and TDD in your discussions. Be prepared to discuss specific projects where you've implemented these practices, as they are crucial for maintaining high-quality code in production.
✨Tip Number 3
Network with professionals in the data science field, especially those who work with machine learning in production settings. Attend meetups or webinars to connect with others and gain insights into best practices and industry trends.
✨Tip Number 4
Prepare to discuss how you can integrate machine learning into business workflows. Think of examples from your past experiences where your work has directly impacted business outcomes, as this will demonstrate your value to the team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, Databricks, and Azure. Include specific projects or roles where you've developed deployment frameworks or automated data science pipelines.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your background in software engineering and machine learning aligns with their needs, particularly in productionising models.
Showcase Relevant Projects: If you have worked on relevant projects, describe them briefly in your application. Focus on your contributions to the data science lifecycle, especially any automation or deployment work you've done.
Highlight Collaboration Skills: Since the role involves working closely with cross-functional teams, emphasise your teamwork and communication skills. Provide examples of how you've successfully collaborated with others in past roles.
How to prepare for a job interview at SPG Resourcing
✨Showcase Your Technical Skills
Make sure to highlight your proficiency in Python, Databricks, and Azure during the interview. Be prepared to discuss specific projects where you've used these technologies, as well as any deployment tools like AKS that you've worked with.
✨Demonstrate Collaboration Experience
Since the role involves working closely with data scientists and cross-functional teams, share examples of how you've successfully collaborated in past projects. Emphasise your ability to communicate complex technical concepts to non-technical stakeholders.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving skills, particularly in automating the data science lifecycle. Be ready to explain your thought process and the steps you would take to tackle common challenges in model deployment and monitoring.
✨Discuss Software Engineering Best Practices
Familiarise yourself with CI/CD, TDD, and other software engineering best practices. Be prepared to discuss how you've applied these principles in your previous work, especially in relation to writing clean, production-ready code.
